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Run this cell to download and install it."]},{"cell_type":"code","metadata":{"id":"VyQblYp0nEZq","executionInfo":{"status":"ok","timestamp":1601455991414,"user_tz":-480,"elapsed":6263,"user":{"displayName":"young Zhao","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuSwIMBj1x2bJOgTT5-3wA8Yt5zgRoz7viNvKn=s64","userId":"11870005096077095950"}},"outputId":"b3f1b5ee-f7d6-4cb3-ddb5-ead5022903cb","colab":{"base_uri":"https://localhost:8080/","height":469}},"source":["!pip install git+https://github.com/deepvision-class/starter-code"],"execution_count":1,"outputs":[{"output_type":"stream","text":["Collecting git+https://github.com/deepvision-class/starter-code\n","  Cloning https://github.com/deepvision-class/starter-code to /tmp/pip-req-build-bdwtyzyc\n","  Running command git clone -q https://github.com/deepvision-class/starter-code /tmp/pip-req-build-bdwtyzyc\n","Requirement already satisfied: pydrive in /usr/local/lib/python3.6/dist-packages (from Colab-Utils==0.1.dev0) (1.3.1)\n","Requirement already satisfied: oauth2client>=4.0.0 in /usr/local/lib/python3.6/dist-packages (from pydrive->Colab-Utils==0.1.dev0) (4.1.3)\n","Requirement already satisfied: google-api-python-client>=1.2 in /usr/local/lib/python3.6/dist-packages (from pydrive->Colab-Utils==0.1.dev0) (1.7.12)\n","Requirement already satisfied: PyYAML>=3.0 in /usr/local/lib/python3.6/dist-packages (from pydrive->Colab-Utils==0.1.dev0) (3.13)\n","Requirement already satisfied: rsa>=3.1.4 in /usr/local/lib/python3.6/dist-packages (from oauth2client>=4.0.0->pydrive->Colab-Utils==0.1.dev0) (4.6)\n","Requirement already satisfied: six>=1.6.1 in /usr/local/lib/python3.6/dist-packages (from oauth2client>=4.0.0->pydrive->Colab-Utils==0.1.dev0) (1.15.0)\n","Requirement already satisfied: httplib2>=0.9.1 in /usr/local/lib/python3.6/dist-packages (from oauth2client>=4.0.0->pydrive->Colab-Utils==0.1.dev0) (0.17.4)\n","Requirement already satisfied: pyasn1>=0.1.7 in /usr/local/lib/python3.6/dist-packages (from oauth2client>=4.0.0->pydrive->Colab-Utils==0.1.dev0) (0.4.8)\n","Requirement already satisfied: pyasn1-modules>=0.0.5 in /usr/local/lib/python3.6/dist-packages (from oauth2client>=4.0.0->pydrive->Colab-Utils==0.1.dev0) (0.2.8)\n","Requirement already satisfied: google-auth-httplib2>=0.0.3 in /usr/local/lib/python3.6/dist-packages (from google-api-python-client>=1.2->pydrive->Colab-Utils==0.1.dev0) (0.0.4)\n","Requirement already satisfied: uritemplate<4dev,>=3.0.0 in /usr/local/lib/python3.6/dist-packages (from google-api-python-client>=1.2->pydrive->Colab-Utils==0.1.dev0) (3.0.1)\n","Requirement already satisfied: google-auth>=1.4.1 in /usr/local/lib/python3.6/dist-packages (from google-api-python-client>=1.2->pydrive->Colab-Utils==0.1.dev0) (1.17.2)\n","Requirement already satisfied: setuptools>=40.3.0 in /usr/local/lib/python3.6/dist-packages (from google-auth>=1.4.1->google-api-python-client>=1.2->pydrive->Colab-Utils==0.1.dev0) (50.3.0)\n","Requirement already satisfied: cachetools<5.0,>=2.0.0 in /usr/local/lib/python3.6/dist-packages (from google-auth>=1.4.1->google-api-python-client>=1.2->pydrive->Colab-Utils==0.1.dev0) (4.1.1)\n","Building wheels for collected packages: Colab-Utils\n","  Building wheel for Colab-Utils (setup.py) ... \u001b[?25l\u001b[?25hdone\n","  Created wheel for Colab-Utils: filename=Colab_Utils-0.1.dev0-cp36-none-any.whl size=10324 sha256=2b4d7d0dcc00c55a7eba1e29e1a8be4f0f1c3b208eeaa1faeb5c38c834e322f2\n","  Stored in directory: /tmp/pip-ephem-wheel-cache-jd31wunz/wheels/63/d1/27/a208931527abb98d326d00209f46c80c9d745851d6a1defd10\n","Successfully built Colab-Utils\n","Installing collected packages: Colab-Utils\n","Successfully installed Colab-Utils-0.1.dev0\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"28bBK1v7nJOu"},"source":["## Setup code\n","Run some setup code for this notebook: Import some useful packages and increase the default figure size."]},{"cell_type":"code","metadata":{"id":"VUCKw4Tl1ddj","executionInfo":{"status":"ok","timestamp":1601455999147,"user_tz":-480,"elapsed":5964,"user":{"displayName":"young Zhao","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuSwIMBj1x2bJOgTT5-3wA8Yt5zgRoz7viNvKn=s64","userId":"11870005096077095950"}}},"source":["from __future__ import print_function\n","from __future__ import division\n","\n","import torch\n","import coutils\n","import random\n","import time\n","import math\n","import matplotlib.pyplot as plt\n","from torchvision.utils import make_grid\n","\n","%matplotlib inline\n","plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n","plt.rcParams['image.interpolation'] = 'nearest'\n","plt.rcParams['image.cmap'] = 'gray'"],"execution_count":2,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"lhqpd2IN2O-K"},"source":["Starting in this assignment, we will use the GPU to accelerate our computation. Run this cell to make sure you are using a GPU."]},{"cell_type":"code","metadata":{"id":"SGDxdBIMRX6b","executionInfo":{"status":"ok","timestamp":1601456001749,"user_tz":-480,"elapsed":1737,"user":{"displayName":"young Zhao","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuSwIMBj1x2bJOgTT5-3wA8Yt5zgRoz7viNvKn=s64","userId":"11870005096077095950"}},"outputId":"728e60b6-7a7d-404b-ecc6-a8500c83017b","colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["if torch.cuda.is_available:\n","  print('Good to go!')\n","else:\n","  print('Please set GPU via Edit -> Notebook Settings.')"],"execution_count":3,"outputs":[{"output_type":"stream","text":["Good to go!\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"fdMnUocj7Srw"},"source":["Now, we will load CIFAR10 dataset, with normalization.\n","\n","In this notebook we will use the **bias trick**: By adding an extra constant feature of ones to each image, we avoid the need to keep track of a bias vector; the bias will be encoded as the part of the weight matrix that interacts with the constant ones in the input.\n","\n","In the `two_layer_net.ipynb` notebook that follows this one, we will not use the bias trick."]},{"cell_type":"code","metadata":{"id":"V2mFlFmQ1ddm","executionInfo":{"status":"ok","timestamp":1601456024284,"user_tz":-480,"elapsed":21736,"user":{"displayName":"young Zhao","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuSwIMBj1x2bJOgTT5-3wA8Yt5zgRoz7viNvKn=s64","userId":"11870005096077095950"}},"outputId":"9b654475-fbe0-48a3-f55f-416adc8ad3b1","colab":{"base_uri":"https://localhost:8080/","height":732,"referenced_widgets":["5b05682b3c35437090dd78e5bf44a441","fe006ebf45be41ad9a2548a7493b5bbf","aa3601f580df4240947d4608f41e9159","73d42facf804491b8b7ca6efdac1fa96","3e2f340dd2924ba1ac1807c7ce1b59e7","44caa32f41c442e7ab28f189ebe0082e","835a76df46d9456a8dc3aa349bc106af","3fde608e8e614ab7b4373a867097ac23"]}},"source":["def get_CIFAR10_data(validation_ratio = 0.02):\n","  \"\"\"\n","  Load the CIFAR-10 dataset from disk and perform preprocessing to prepare\n","  it for the linear classifier. These are the same steps as we used for the\n","  SVM, but condensed to a single function.  \n","  \"\"\"\n","  X_train, y_train, X_test, y_test = coutils.data.cifar10()\n","\n","  # Move all the data to the GPU\n","  X_train = X_train.cuda()\n","  y_train = y_train.cuda()\n","  X_test = X_test.cuda()\n","  y_test = y_test.cuda()\n","\n","  # 0. Visualize some examples from the dataset.\n","  class_names = [\n","      'plane', 'car', 'bird', 'cat', 'deer',\n","      'dog', 'frog', 'horse', 'ship', 'truck'\n","  ]\n","  img = coutils.utils.visualize_dataset(X_train, y_train, 12, class_names)\n","  plt.imshow(img)\n","  plt.axis('off')\n","  plt.show()\n","\n","  # 1. Normalize the data: subtract the mean RGB (zero mean)\n","  mean_image = X_train.mean(dim=0, keepdim=True).mean(dim=2, keepdim=True).mean(dim=3, keepdim=True)\n","  X_train -= mean_image\n","  X_test -= mean_image\n","\n","  # 2. Reshape the image data into rows\n","  X_train = X_train.reshape(X_train.shape[0], -1)\n","  X_test = X_test.reshape(X_test.shape[0], -1)\n","\n","  # 3. Add bias dimension and transform into columns\n","  ones_train = torch.ones(X_train.shape[0], 1, device=X_train.device)\n","  X_train = torch.cat([X_train, ones_train], dim=1)\n","  ones_test = torch.ones(X_test.shape[0], 1, device=X_test.device)\n","  X_test = torch.cat([X_test, ones_test], dim=1)\n","\n","  # 4. Carve out part of the training set to use for validation.\n","  # For random permutation, you can use torch.randperm or torch.randint\n","  # But, for this homework, we use slicing instead.\n","  num_training = int( X_train.shape[0] * (1.0 - validation_ratio) )\n","  num_validation = X_train.shape[0] - num_training\n","\n","  # Return the dataset as a dictionary\n","  data_dict = {}\n","\n","  data_dict['X_val'] = X_train[num_training:num_training + num_validation]\n","  data_dict['y_val'] = y_train[num_training:num_training + num_validation]\n","  \n","  data_dict['X_train'] = X_train[0:num_training]\n","  data_dict['y_train'] = y_train[0:num_training]\n","\n","  data_dict['X_test'] = X_test\n","  data_dict['y_test'] = y_test\n","  return data_dict\n","\n","# Invoke the above function to get our data.\n","data_dict = get_CIFAR10_data()\n","print('Train data shape: ', data_dict['X_train'].shape)\n","print('Train labels shape: ', data_dict['y_train'].shape)\n","print('Validation data shape: ', data_dict['X_val'].shape)\n","print('Validation labels shape: ', data_dict['y_val'].shape)\n","print('Test data shape: ', data_dict['X_test'].shape)\n","print('Test labels shape: ', data_dict['y_test'].shape)"],"execution_count":4,"outputs":[{"output_type":"stream","text":["Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./cifar-10-python.tar.gz\n"],"name":"stdout"},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"5b05682b3c35437090dd78e5bf44a441","version_minor":0,"version_major":2},"text/plain":["HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["Extracting ./cifar-10-python.tar.gz to .\n"],"name":"stdout"},{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/coutils/utils.py:54: UserWarning: This overload of nonzero is deprecated:\n","\tnonzero()\n","Consider using one of the following signatures instead:\n","\tnonzero(*, bool as_tuple) (Triggered internally at  /pytorch/torch/csrc/utils/python_arg_parser.cpp:766.)\n","  idxs = (y_data == y).nonzero().view(-1)\n"],"name":"stderr"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 720x576 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["Train data shape:  torch.Size([49000, 3073])\n","Train labels shape:  torch.Size([49000])\n","Validation data shape:  torch.Size([1000, 3073])\n","Validation labels shape:  torch.Size([1000])\n","Test data shape:  torch.Size([10000, 3073])\n","Test labels shape:  torch.Size([10000])\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"MhOuO4zE9ONc"},"source":["For Softmax and SVM, we will analytically compute the gradient, as a sanity check."]},{"cell_type":"code","metadata":{"id":"biGULpeRxEhe","executionInfo":{"status":"ok","timestamp":1601456027968,"user_tz":-480,"elapsed":1035,"user":{"displayName":"young Zhao","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuSwIMBj1x2bJOgTT5-3wA8Yt5zgRoz7viNvKn=s64","userId":"11870005096077095950"}}},"source":["def grad_check_sparse(f, x, analytic_grad, num_checks=10, h=1e-7):\n","  \"\"\"\n","  Utility function to perform numeric gradient checking. We use the centered\n","  difference formula to compute a numeric derivative:\n","  \n","  f'(x) =~ (f(x + h) - f(x - h)) / (2h)\n","\n","  Rather than computing a full numeric gradient, we sparsely sample a few\n","  dimensions along which to compute numeric derivatives.\n","\n","  Inputs:\n","  - f: A function that inputs a torch tensor and returns a torch scalar\n","  - x: A torch tensor giving the point at which to evaluate the numeric gradient\n","  - analytic_grad: A torch tensor giving the analytic gradient of f at x\n","  - num_checks: The number of dimensions along which to check\n","  - h: Step size for computing numeric derivatives\n","  \"\"\"\n","  # fix random seed for \n","  coutils.utils.fix_random_seed()\n","\n","  for i in range(num_checks):\n","    \n","    ix = tuple([random.randrange(m) for m in x.shape])\n","    \n","    oldval = x[ix].item()\n","    x[ix] = oldval + h # increment by h\n","\n","    fxph = f(x).item() # evaluate f(x + h)\n","    x[ix] = oldval - h # increment by h\n","\n","    fxmh = f(x).item() # evaluate f(x - h)\n","    x[ix] = oldval     # reset\n","\n","    grad_numerical = (fxph - fxmh) / (2 * h)\n","    grad_analytic = analytic_grad[ix]\n","    rel_error_top = abs(grad_numerical - grad_analytic)\n","    rel_error_bot = (abs(grad_numerical) + abs(grad_analytic) + 1e-12)\n","    rel_error = rel_error_top / rel_error_bot\n","    msg = 'numerical: %f analytic: %f, relative error: %e'\n","    print(msg % (grad_numerical, grad_analytic, rel_error))"],"execution_count":5,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"0Lvdm4fm7iJC"},"source":["## SVM Classifier\n","\n","In this section, you will:\n","    \n","- implement a fully-vectorized **loss function** for the SVM\n","- implement the fully-vectorized expression for its **analytic gradient**\n","- **check your implementation** using numerical gradient\n","- use a validation set to **tune the learning rate and regularization** strength\n","- **optimize** the loss function with **SGD**\n","- **visualize** the final learned weights\n","\n","> Multiclass SVM Loss \n","\n","> The score of the correct class should be higher than all\n","the other scores\n"]},{"cell_type":"code","metadata":{"id":"fjo142guIikY","executionInfo":{"status":"ok","timestamp":1601456035497,"user_tz":-480,"elapsed":937,"user":{"displayName":"young Zhao","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuSwIMBj1x2bJOgTT5-3wA8Yt5zgRoz7viNvKn=s64","userId":"11870005096077095950"}},"outputId":"a44e7223-c08a-4acc-d40d-d416e5d2bf80","colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["import torch\n","x = torch.tensor([1,1,1,1],dtype=torch.float32)\n","w1 = torch.tensor([1,0,0,0],dtype=torch.float32)\n","w2 = torch.tensor([0.25,0.25,0.25,0.25],dtype=torch.float32)\n","\n","w1x = w1.dot(x)\n","w2x = w2.dot(x) \n","print(w1x, w2x)"],"execution_count":6,"outputs":[{"output_type":"stream","text":["tensor(1.) tensor(1.)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"3CFWcoJF8hti","executionInfo":{"status":"ok","timestamp":1601456039798,"user_tz":-480,"elapsed":951,"user":{"displayName":"young Zhao","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuSwIMBj1x2bJOgTT5-3wA8Yt5zgRoz7viNvKn=s64","userId":"11870005096077095950"}}},"source":["def svm_loss_naive(W, X, y, reg):\n","  \"\"\"\n","  Structured SVM loss function, naive implementation (with loops).\n","\n","  Inputs have dimension D, there are C classes, and we operate on minibatches\n","  of N examples. When you implment the regularization over W, please DO NOT\n","  multiply the regularization term by 1/2 (no coefficient). \n","\n","  Inputs:\n","  - W: A PyTorch tensor of shape (D, C) containing weights.\n","  - X: A PyTorch tensor of shape (N, D) containing a minibatch of data.\n","  - y: A PyTorch tensor of shape (N,) containing training labels; y[i] = c means\n","    that X[i] has label c, where 0 <= c < C.\n","  - reg: (float) regularization strength\n","\n","  Returns a tuple of:\n","  - loss as torch scalar\n","  - gradient of loss with respect to weights W; a tensor of same shape as W\n","  \"\"\"\n","  dW = torch.zeros_like(W) # initialize the gradient as zero\n","\n","  # compute the loss and the gradient\n","  num_classes = W.shape[1]\n","  num_train = X.shape[0]\n","  loss = 0.0\n","\n","  for i in range(num_train):\n","    scores = W.t().mv(X[i])\n","    correct_class_score = scores[y[i]]\n","    for j in range(num_classes):\n","      if j == y[i]:\n","        continue\n","\n","      margin = scores[j] - correct_class_score + 1 # note delta = 1\n","      if margin > 0:\n","        loss += margin\n","        #######################################################################\n","        # TODO:                                                               #\n","        # Compute the gradient of the loss function and store it dW. (part 1) #\n","        # Rather that first computing the loss and then computing the         #\n","        # derivative, it is simple to compute the derivative at the same time #\n","        # that the loss is being computed.                                    #\n","        #######################################################################\n","        # Replace \"pass\" statement with your code\n","        dW[:, y[i]] += -X[i, :].t()\n","        dW[:, j] += X[i, :].t()\n","        #######################################################################\n","        #                       END OF YOUR CODE                              #\n","        #######################################################################\n","        \n","\n","  # Right now the loss is a sum over all training examples, but we want it\n","  # to be an average instead so we divide by num_train.\n","  loss /= num_train\n","\n","  # Add regularization to the loss.\n","  loss += reg * torch.sum(W * W)\n","\n","  #############################################################################\n","  # TODO:                                                                     #\n","  # Compute the gradient of the loss function and store it in dW. (part 2)    #\n","  #############################################################################\n","  # Replace \"pass\" statement with your code\n","  dW = dW / num_train + reg * 2 * W\n","  #############################################################################\n","  #                             END OF YOUR CODE                              #\n","  #############################################################################\n","\n","  return loss, dW"],"execution_count":7,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"xTI4qN7S9aTr"},"source":["Evaluate the naive implementation of the loss we provided for you. You will get around 9.000175."]},{"cell_type":"code","metadata":{"id":"Hxzu2uZq9P8P","executionInfo":{"status":"ok","timestamp":1601456046712,"user_tz":-480,"elapsed":3421,"user":{"displayName":"young Zhao","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuSwIMBj1x2bJOgTT5-3wA8Yt5zgRoz7viNvKn=s64","userId":"11870005096077095950"}},"outputId":"7a119f3e-493b-4c6c-a31a-736af386a42f","colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["# generate a random SVM weight tensor of small numbers\n","coutils.utils.fix_random_seed()\n","W = torch.randn(3073, 10, device=data_dict['X_val'].device) * 0.0001 \n","\n","loss, grad = svm_loss_naive(W, data_dict['X_val'], data_dict['y_val'], 0.000005)\n","print('loss: %f' % (loss, ))"],"execution_count":8,"outputs":[{"output_type":"stream","text":["loss: 9.000433\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"9LbRTXJ39Yp8"},"source":["The `grad` returned from the function above is right now all zero. Derive and implement the gradient for the SVM cost function and implement it inline inside the function `svm_loss_naive`. You will find it helpful to interleave your new code inside the existing function.\n","\n","To check that you have implemented the gradient correctly, you can numerically estimate the gradient of the loss function and compare the numeric estimate to the gradient that you computed. We have provided code that does this for you (The relative errors should be less than `1e-6`)."]},{"cell_type":"code","metadata":{"id":"o3sMha4i9p_V","executionInfo":{"status":"ok","timestamp":1601456053822,"user_tz":-480,"elapsed":4010,"user":{"displayName":"young Zhao","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuSwIMBj1x2bJOgTT5-3wA8Yt5zgRoz7viNvKn=s64","userId":"11870005096077095950"}},"outputId":"77c3b755-a522-487e-d8aa-0eb3e3066523","colab":{"base_uri":"https://localhost:8080/","height":197}},"source":["# Once you've implemented the gradient, recompute it with the code below\n","# and gradient check it with the function we provided for you\n","\n","# Use a random W and a minibatch of data from the val set for gradient checking\n","# For numeric gradient checking it is a good idea to use 64-bit floating point\n","# numbers for increased numeric precision; however when actually training models\n","# we usually use 32-bit floating point numbers for increased speed.\n","coutils.utils.fix_random_seed()\n","W = 0.0001 * torch.randn(3073, 10, device=data_dict['X_val'].device).double()\n","batch_size = 64\n","X_batch = data_dict['X_val'][:64].double()\n","y_batch = data_dict['y_val'][:64]\n","\n","# Compute the loss and its gradient at W.\n","loss, grad = svm_loss_naive(W.double(), X_batch, y_batch, reg=0.0) \n","\n","# Numerically compute the gradient along several randomly chosen dimensions, and\n","# compare them with your analytically computed gradient. The numbers should\n","# match almost exactly along all dimensions.\n","f = lambda w: svm_loss_naive(w, X_batch, y_batch, reg=0.0)[0]\n","grad_numerical = grad_check_sparse(f, W.double(), grad)"],"execution_count":9,"outputs":[{"output_type":"stream","text":["numerical: -0.034577 analytic: -0.034577, relative error: 6.225541e-07\n","numerical: 0.126951 analytic: 0.126951, relative error: 1.275037e-08\n","numerical: -0.068597 analytic: -0.068597, relative error: 3.398665e-08\n","numerical: 0.025717 analytic: 0.025717, relative error: 4.062602e-08\n","numerical: 0.048266 analytic: 0.048266, relative error: 1.748268e-07\n","numerical: 0.052260 analytic: 0.052260, relative error: 9.239161e-08\n","numerical: 0.096133 analytic: 0.096133, relative error: 8.770072e-08\n","numerical: 0.032702 analytic: 0.032702, relative error: 1.787458e-07\n","numerical: -0.117158 analytic: -0.117158, relative error: 3.574265e-08\n","numerical: -0.154093 analytic: -0.154093, relative error: 3.717856e-08\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"lSdsG-L292Ww"},"source":["Let's do the gradient check once again with regularization turned on. (You didn't forget the regularization gradient, did you?)\n","\n","You should see relative errors less than `1e-5`."]},{"cell_type":"code","metadata":{"id":"bH6lXxVn9xZk","executionInfo":{"status":"ok","timestamp":1601456060494,"user_tz":-480,"elapsed":3535,"user":{"displayName":"young Zhao","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuSwIMBj1x2bJOgTT5-3wA8Yt5zgRoz7viNvKn=s64","userId":"11870005096077095950"}},"outputId":"b49426ec-90aa-4bce-a107-75e5554393e4","colab":{"base_uri":"https://localhost:8080/","height":197}},"source":["# Use a minibatch of data from the val set for gradient checking\n","coutils.utils.fix_random_seed()\n","W = 0.0001 * torch.randn(3073, 10, device=data_dict['X_val'].device).double()\n","batch_size = 64\n","X_batch = data_dict['X_val'][:64].double()\n","y_batch = data_dict['y_val'][:64]\n","\n","# Compute the loss and its gradient at W.\n","loss, grad = svm_loss_naive(W.double(), X_batch, y_batch, reg=1e3) \n","\n","# Numerically compute the gradient along several randomly chosen dimensions, and\n","# compare them with your analytically computed gradient. The numbers should\n","# match almost exactly along all dimensions.\n","f = lambda w: svm_loss_naive(w, X_batch, y_batch, reg=1e3)[0]\n","grad_numerical = grad_check_sparse(f, W.double(), grad)"],"execution_count":10,"outputs":[{"output_type":"stream","text":["numerical: -0.121624 analytic: -0.121624, relative error: 1.811484e-07\n","numerical: 0.020923 analytic: 0.020923, relative error: 9.134296e-08\n","numerical: -0.076604 analytic: -0.076604, relative error: 2.006599e-08\n","numerical: 0.256069 analytic: 0.256069, relative error: 8.151044e-09\n","numerical: -0.330089 analytic: -0.330089, relative error: 2.819906e-08\n","numerical: 0.004713 analytic: 0.004713, relative error: 8.515910e-07\n","numerical: 0.101968 analytic: 0.101968, relative error: 6.157842e-08\n","numerical: 0.097235 analytic: 0.097235, relative error: 6.655515e-08\n","numerical: -0.117112 analytic: -0.117112, relative error: 4.635519e-08\n","numerical: -0.257260 analytic: -0.257260, relative error: 2.476319e-08\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"OeOlF_YJ-ApZ"},"source":["Now, let's implement vectorized version of SVM: `svm_loss_vectorized`. It should compute the same inputs and outputs as the naive version before, but it should involve **no explicit loops**."]},{"cell_type":"code","metadata":{"id":"yYrN2JA0-Aoa","executionInfo":{"status":"ok","timestamp":1601456066034,"user_tz":-480,"elapsed":1166,"user":{"displayName":"young Zhao","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuSwIMBj1x2bJOgTT5-3wA8Yt5zgRoz7viNvKn=s64","userId":"11870005096077095950"}}},"source":["def svm_loss_vectorized(W, X, y, reg):\n","  \"\"\"\n","  Structured SVM loss function, vectorized implementation. When you implment \n","  the regularization over W, please DO NOT multiply the regularization term by \n","  1/2 (no coefficient). \n","\n","  Inputs and outputs are the same as svm_loss_naive.\n","  \"\"\"\n","  loss = 0.0\n","  dW = torch.zeros_like(W) # initialize the gradient as zero\n","\n","  #############################################################################\n","  # TODO:                                                                     #\n","  # Implement a vectorized version of the structured SVM loss, storing the    #\n","  # result in loss.                                                           #\n","  #############################################################################\n","  # Replace \"pass\" statement with your code\n","  num_train, num_classes = X.shape[0], W.shape[1]\n","  scores = X.mm(W)\n","  correct_label_score_idxes = (range(scores.shape[0]), y)\n","\n","  correct_label_score = scores[correct_label_score_idxes]\n","  scores_diff = scores - correct_label_score.reshape(-1,1)\n","\n","  scores_diff += 1\n","  scores_diff[correct_label_score_idxes] = 0\n","  indxes_of_neg_nums = torch.nonzero(scores_diff < 0)\n","  scores_diff[indxes_of_neg_nums] = 0\n","\n","  loss = scores_diff.sum()\n","  loss /= num_train\n","\n","  loss += reg * (W * W).sum()\n","  #############################################################################\n","  #                             END OF YOUR CODE                              #\n","  #############################################################################\n","\n","\n","  #############################################################################\n","  # TODO:                                                                     #\n","  # Implement a vectorized version of the gradient for the structured SVM     #\n","  # loss, storing the result in dW.                                           #\n","  #                                                                           #\n","  # Hint: Instead of computing the gradient from scratch, it may be easier    #\n","  # to reuse some of the intermediate values that you used to compute the     #\n","  # loss.                                                                     #\n","  #############################################################################\n","  # Replace \"pass\" statement with your code\n","  scores_diff[scores_diff > 0] = 1\n","  correct_label_vals = torch.sum(scores_diff, 1) * -1\n","  scores_diff[correct_label_score_idxes] = correct_label_vals\\\n","  \n","  dW = X.t().mm(scores_diff)\n","  dW /= num_train\n","\n","  dW += reg * 2 * W\n","  \n","  #############################################################################\n","  #                             END OF YOUR CODE                              #\n","  #############################################################################\n","\n","  return loss, dW"],"execution_count":11,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"sc5Wtu-e-WlI"},"source":["Let's first check the speed and performance bewteen the non-vectorized and the vectorized version. You should see a speedup of more than 100x.\n","\n","(Note: It may have some difference, but should be less than 1e-6)"]},{"cell_type":"code","metadata":{"id":"pBLqLTAGo1Rs","executionInfo":{"status":"ok","timestamp":1601456078488,"user_tz":-480,"elapsed":1105,"user":{"displayName":"young Zhao","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuSwIMBj1x2bJOgTT5-3wA8Yt5zgRoz7viNvKn=s64","userId":"11870005096077095950"}},"outputId":"083bd67d-58e5-47f0-efd9-0b705bb91bd7","colab":{"base_uri":"https://localhost:8080/","height":89}},"source":["# Next implement the function svm_loss_vectorized; for now only compute the loss;\n","# we will implement the gradient in a moment.\n","\n","# Use random weights and a minibatch of val data for gradient checking\n","coutils.utils.fix_random_seed()\n","W = 0.0001 * torch.randn(3073, 10, device=data_dict['X_val'].device).double()\n","X_batch = data_dict['X_val'][:128].double()\n","y_batch = data_dict['y_val'][:128]\n","reg = 0.000005\n","\n","# Run and time the naive version\n","torch.cuda.synchronize()\n","tic = time.time()\n","loss_naive, grad_naive = svm_loss_naive(W, X_batch, y_batch, reg)\n","torch.cuda.synchronize()\n","toc = time.time()\n","ms_naive = 1000.0 * (toc - tic)\n","print('Naive loss: %e computed in %.2fms' % (loss_naive, ms_naive))\n","\n","# Run and time the vectorized version\n","torch.cuda.synchronize()\n","tic = time.time()\n","loss_vec, _ = svm_loss_vectorized(W, X_batch, y_batch, reg)\n","torch.cuda.synchronize()\n","toc = time.time()\n","ms_vec = 1000.0 * (toc - tic)\n","print('Vectorized loss: %e computed in %.2fms' % (loss_vec, ms_vec))\n","\n","# The losses should match but your vectorized implementation should be much faster.\n","print('Difference: %.2e' % (loss_naive - loss_vec))\n","print('Speedup: %.2fX' % (ms_naive / ms_vec))"],"execution_count":12,"outputs":[{"output_type":"stream","text":["Naive loss: 9.000144e+00 computed in 273.36ms\n","Vectorized loss: 9.000144e+00 computed in 8.59ms\n","Difference: 1.78e-15\n","Speedup: 31.82X\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"cRDPpAMl-0WD"},"source":["Then, let's compute the gradient of the loss function. We can check the difference of gradient as well. (The error should be less than 1e-6)\n","\n","Now implement a vectorized version of the gradient computation in `svm_loss_vectorize` above. Run the cell below to compare the gradient of your naive and vectorized implementations. The difference between the gradients should be less than `1e-6`, and the vectorized version should run at least 100x faster.\n"]},{"cell_type":"code","metadata":{"id":"3_SyWrTJ-OfX","executionInfo":{"status":"ok","timestamp":1601456082852,"user_tz":-480,"elapsed":976,"user":{"displayName":"young Zhao","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuSwIMBj1x2bJOgTT5-3wA8Yt5zgRoz7viNvKn=s64","userId":"11870005096077095950"}},"outputId":"07164794-5ce7-4d07-8037-0884c7714016","colab":{"base_uri":"https://localhost:8080/","height":89}},"source":["# The naive implementation and the vectorized implementation should match, but\n","# the vectorized version should still be much faster.\n","\n","# Use random weights and a minibatch of val data for gradient checking\n","coutils.utils.fix_random_seed()\n","W = 0.0001 * torch.randn(3073, 10, device=data_dict['X_val'].device).double()\n","X_batch = data_dict['X_val'][:128].double()\n","y_batch = data_dict['y_val'][:128]\n","reg = 0.000005\n","\n","# Run and time the naive version\n","torch.cuda.synchronize()\n","tic = time.time()\n","_, grad_naive = svm_loss_naive(W, X_batch, y_batch, 0.000005)\n","torch.cuda.synchronize()\n","toc = time.time()\n","ms_naive = 1000.0 * (toc - tic)\n","print('Naive loss and gradient: computed in %.2fms' % ms_naive)\n","\n","# Run and time the vectorized version\n","torch.cuda.synchronize()\n","tic = time.time()\n","_, grad_vec = svm_loss_vectorized(W, X_batch, y_batch, 0.000005)\n","torch.cuda.synchronize()\n","toc = time.time()\n","ms_vec = 1000.0 * (toc - tic)\n","print('Vectorized loss and gradient: computed in %.2fms' % ms_vec)\n","\n","# The loss is a single number, so it is easy to compare the values computed\n","# by the two implementations. The gradient on the other hand is a tensor, so\n","# we use the Frobenius norm to compare them.\n","grad_difference = torch.norm(grad_naive - grad_vec, p='fro')\n","print('Gradient difference: %.2e' % grad_difference)\n","print('Speedup: %.2fX' % (ms_naive / ms_vec))"],"execution_count":13,"outputs":[{"output_type":"stream","text":["Naive loss and gradient: computed in 273.75ms\n","Vectorized loss and gradient: computed in 1.60ms\n","Gradient difference: 0.00e+00\n","Speedup: 170.81X\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"uU852IitCtrC"},"source":["Now that we have an efficient vectorized implementation of the SVM loss and its gradient, we can implement a training pipeline for linear classifiers.\n","\n","Complete the implementation of the following function:\n"]},{"cell_type":"code","metadata":{"id":"YeRAY8TZAK22","executionInfo":{"status":"ok","timestamp":1601456086586,"user_tz":-480,"elapsed":1136,"user":{"displayName":"young Zhao","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuSwIMBj1x2bJOgTT5-3wA8Yt5zgRoz7viNvKn=s64","userId":"11870005096077095950"}}},"source":["def train_linear_classifier(loss_func, W, X, y, learning_rate=1e-3, \n","                            reg=1e-5, num_iters=100, batch_size=200, verbose=False):\n","  \"\"\"\n","  Train this linear classifier using stochastic gradient descent.\n","\n","  Inputs:\n","  - loss_func: loss function to use when training. It should take W, X, y\n","    and reg as input, and output a tuple of (loss, dW)\n","  - W: A PyTorch tensor of shape (D, C) giving the initial weights of the\n","    classifier. If W is None then it will be initialized here.\n","  - X: A PyTorch tensor of shape (N, D) containing training data; there are N\n","    training samples each of dimension D.\n","  - y: A PyTorch tensor of shape (N,) containing training labels; y[i] = c\n","    means that X[i] has label 0 <= c < C for C classes.\n","  - learning_rate: (float) learning rate for optimization.\n","  - reg: (float) regularization strength.\n","  - num_iters: (integer) number of steps to take when optimizing\n","  - batch_size: (integer) number of training examples to use at each step.\n","  - verbose: (boolean) If true, print progress during optimization.\n","\n","  Returns: A tuple of:\n","  - W: The final value of the weight matrix and the end of optimization\n","  - loss_history: A list of Python scalars giving the values of the loss at each\n","    training iteration.\n","  \"\"\"\n","  # assume y takes values 0...K-1 where K is number of classes\n","  num_classes = torch.max(y) + 1\n","  num_train, dim = X.shape\n","  if W is None:\n","    # lazily initialize W\n","    W = 0.000001 * torch.randn(dim, num_classes, device=X.device, dtype=X.dtype)  # dim x num_classes\n","\n","  # Run stochastic gradient descent to optimize W\n","  loss_history = []\n","  for it in range(num_iters):\n","    X_batch = None\n","    y_batch = None\n","    #########################################################################\n","    # TODO:                                                                 #\n","    # Sample batch_size elements from the training data and their           #\n","    # corresponding labels to use in this round of gradient descent.        #\n","    # Store the data in X_batch and their corresponding labels in           #\n","    # y_batch; after sampling, X_batch should have shape (batch_size, dim)  #\n","    # and y_batch should have shape (batch_size,)                           #\n","    #                                                                       #\n","    # Hint: Use torch.randint to generate indices.                          #\n","    #########################################################################\n","    # Replace \"pass\" statement with your code\n","    batch_idxes = torch.randint(num_train, (batch_size,))\n","    X_batch = X[batch_idxes, :]\n","    y_batch = y[batch_idxes]\n","    #########################################################################\n","    #                       END OF YOUR CODE                                #\n","    #########################################################################\n","\n","    # evaluate loss and gradient\n","    loss, grad = loss_func(W, X_batch, y_batch, reg)\n","    loss_history.append(loss.item())\n","\n","    # perform parameter update\n","    #########################################################################\n","    # TODO:                                                                 #\n","    # Update the weights using the gradient and the learning rate.          #\n","    #########################################################################\n","    # Replace \"pass\" statement with your code\n","    W -= learning_rate * grad\n","    #########################################################################\n","    #                       END OF YOUR CODE                                #\n","    #########################################################################\n","\n","    if verbose and it % 100 == 0:\n","      print('iteration %d / %d: loss %f' % (it, num_iters, loss))\n","\n","  return W, loss_history"],"execution_count":14,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"c6IL1_D9wCbF"},"source":["Once you have implemented the training function, run the following cell to train a linear classifier using some default hyperparameters:\n","\n","(You should see a final loss close to 9.0, and your training loop should run in about two seconds)"]},{"cell_type":"code","metadata":{"id":"QaEZkCe3-kOu","executionInfo":{"status":"ok","timestamp":1601456092166,"user_tz":-480,"elapsed":2048,"user":{"displayName":"young Zhao","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuSwIMBj1x2bJOgTT5-3wA8Yt5zgRoz7viNvKn=s64","userId":"11870005096077095950"}},"outputId":"6ff35724-2ed7-49e0-81b3-2cfe35b0aa24","colab":{"base_uri":"https://localhost:8080/","height":305}},"source":["# fix random seed before we perform this operation\n","coutils.utils.fix_random_seed()\n","\n","torch.cuda.synchronize()\n","tic = time.time()\n","\n","W, loss_hist = train_linear_classifier(svm_loss_vectorized, None, \n","                                       data_dict['X_train'], \n","                                       data_dict['y_train'], \n","                                       learning_rate=3e-11, reg=2.5e4,\n","                                       num_iters=1500, verbose=True)\n","\n","torch.cuda.synchronize()\n","toc = time.time()\n","print('That took %fs' % (toc - tic))"],"execution_count":15,"outputs":[{"output_type":"stream","text":["iteration 0 / 1500: loss 9.000759\n","iteration 100 / 1500: loss 9.000790\n","iteration 200 / 1500: loss 9.000771\n","iteration 300 / 1500: loss 9.000764\n","iteration 400 / 1500: loss 9.000766\n","iteration 500 / 1500: loss 9.000767\n","iteration 600 / 1500: loss 9.000770\n","iteration 700 / 1500: loss 9.000748\n","iteration 800 / 1500: loss 9.000766\n","iteration 900 / 1500: loss 9.000761\n","iteration 1000 / 1500: loss 9.000764\n","iteration 1100 / 1500: loss 9.000766\n","iteration 1200 / 1500: loss 9.000779\n","iteration 1300 / 1500: loss 9.000757\n","iteration 1400 / 1500: loss 9.000749\n","That took 1.399858s\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"n8mz9aXfDrsF"},"source":["A useful debugging strategy is to plot the loss as a function of iteration number. In this case it seems our hyperparameters are not good, since the training loss is not decreasing very fast.\n","\n"]},{"cell_type":"code","metadata":{"id":"JJ8GjaZS_MLe","executionInfo":{"status":"ok","timestamp":1601456095758,"user_tz":-480,"elapsed":1213,"user":{"displayName":"young Zhao","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuSwIMBj1x2bJOgTT5-3wA8Yt5zgRoz7viNvKn=s64","userId":"11870005096077095950"}},"outputId":"27397c58-a603-4248-993a-08621c5d159d","colab":{"base_uri":"https://localhost:8080/","height":508}},"source":["plt.plot(loss_hist, 'o')\n","plt.xlabel('Iteration number')\n","plt.ylabel('Loss value')\n","plt.show()"],"execution_count":16,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 720x576 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"hBzZNGNoD18s"},"source":["Let's move on to the prediction stage:"]},{"cell_type":"code","metadata":{"id":"UdvAk57DD0xT","executionInfo":{"status":"ok","timestamp":1601456100172,"user_tz":-480,"elapsed":1143,"user":{"displayName":"young Zhao","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuSwIMBj1x2bJOgTT5-3wA8Yt5zgRoz7viNvKn=s64","userId":"11870005096077095950"}}},"source":["def predict_linear_classifier(W, X):\n","  \"\"\"\n","  Use the trained weights of this linear classifier to predict labels for\n","  data points.\n","\n","  Inputs:\n","  - W: A PyTorch tensor of shape (D, C), containing weights of a model\n","  - X: A PyTorch tensor of shape (N, D) containing training data; there are N\n","    training samples each of dimension D.\n","\n","  Returns:\n","  - y_pred: PyTorch int64 tensor of shape (N,) giving predicted labels for each\n","    elemment of X. Each element of y_pred should be between 0 and C - 1.\n","  \"\"\"\n","  y_pred = torch.zeros(X.shape[0]) # N x 1\n","  ###########################################################################\n","  # TODO:                                                                   #\n","  # Implement this method. Store the predicted labels in y_pred.            #\n","  ###########################################################################\n","  # Replace \"pass\" statement with your code\n","  class_pred = X.mm(W)\n","  y_pred = torch.argmax(class_pred, dim=1)\n","  ###########################################################################\n","  #                           END OF YOUR CODE                              #\n","  ###########################################################################\n","  return y_pred"],"execution_count":17,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"DRdfknKsE6F2"},"source":["Then, let's evaluate the performance our trained model on both the training and validation set. You should see validation accuracy less than 10%."]},{"cell_type":"code","metadata":{"id":"YfToPzce_OBH","executionInfo":{"status":"ok","timestamp":1601348840589,"user_tz":-480,"elapsed":857,"user":{"displayName":"young Zhao","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuSwIMBj1x2bJOgTT5-3wA8Yt5zgRoz7viNvKn=s64","userId":"11870005096077095950"}},"outputId":"417e4c63-afd7-4fb8-ff68-d0cc4a74f3a0","colab":{"base_uri":"https://localhost:8080/","height":53}},"source":["# evaluate the performance on both the training and validation set\n","y_train_pred = predict_linear_classifier(W, data_dict['X_train'])\n","train_acc = 100.0 * (data_dict['y_train'] == y_train_pred).float().mean().item()\n","print('Training accuracy: %.2f%%' % train_acc)\n","y_val_pred = predict_linear_classifier(W, data_dict['X_val'])\n","val_acc = 100.0 * (data_dict['y_val'] == y_val_pred).float().mean().item()\n","print('Validation accuracy: %.2f%%' % val_acc)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Training accuracy: 10.24%\n","Validation accuracy: 10.20%\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"VWIyGnMOFOV8"},"source":["Unfortunately, the performance of our initial model is quite bad. To find a better hyperparamters, let's first modulize the functions that we've implemented."]},{"cell_type":"code","metadata":{"id":"A1SfNxVs8OBv","executionInfo":{"status":"ok","timestamp":1601456111332,"user_tz":-480,"elapsed":1758,"user":{"displayName":"young Zhao","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuSwIMBj1x2bJOgTT5-3wA8Yt5zgRoz7viNvKn=s64","userId":"11870005096077095950"}}},"source":["# Note: We will re-use `LinearClassifier' in Softmax section\n","class LinearClassifier(object):\n","  \n","  def __init__(self):\n","    self.W = None\n","    \n","  def train(self, X_train, y_train, learning_rate=1e-3, reg=1e-5, num_iters=100,\n","            batch_size=200, verbose=False):\n","    train_args = (self.loss, self.W, X_train, y_train, learning_rate, reg,\n","                  num_iters, batch_size, verbose)\n","    self.W, loss_history = train_linear_classifier(*train_args)\n","    return loss_history\n","\n","  def predict(self, X):\n","    return predict_linear_classifier(self.W, X) \n","  \n","  def loss(self, W, X_batch, y_batch, reg):\n","    \"\"\"\n","    Compute the loss function and its derivative. \n","    Subclasses will override this.\n","\n","    Inputs:\n","    - W: A PyTorch tensor of shape (D, C) containing (trained) weight of a model.\n","    - X_batch: A PyTorch tensor of shape (N, D) containing a minibatch of N\n","      data points; each point has dimension D.\n","    - y_batch: A PyTorch tensor of shape (N,) containing labels for the minibatch.\n","    - reg: (float) regularization strength.\n","\n","    Returns: A tuple containing:\n","    - loss as a single float\n","    - gradient with respect to self.W; an tensor of the same shape as W\n","    \"\"\"\n","    pass\n","    \n","  def _loss(self, X_batch, y_batch, reg):\n","    self.loss(self.W, X_batch, y_batch, reg)\n","\n","  \n","class LinearSVM(LinearClassifier):\n","  \"\"\" A subclass that uses the Multiclass SVM loss function \"\"\"\n","\n","  def loss(self, W, X_batch, y_batch, reg):\n","    return svm_loss_vectorized(W, X_batch, y_batch, reg)"],"execution_count":18,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"taNmjt2wGJQr"},"source":["Now, please use the validation set to tune hyperparameters (regularization strength and learning rate). You should experiment with different ranges for the learning rates and regularization strengths.\n","\n","To get full credit for the assignment your best model found through cross-validation should achieve an accuracy of at least 37% on the validation set.\n","\n","(Our best model got over 40% -- did you beat us?)"]},{"cell_type":"code","metadata":{"id":"oVMsJ9Ti_Ude","executionInfo":{"status":"ok","timestamp":1601456133047,"user_tz":-480,"elapsed":17221,"user":{"displayName":"young Zhao","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuSwIMBj1x2bJOgTT5-3wA8Yt5zgRoz7viNvKn=s64","userId":"11870005096077095950"}},"outputId":"d4eecdf0-ce18-46b3-d740-003727bf5026","colab":{"base_uri":"https://localhost:8080/","height":197}},"source":["# results is dictionary mapping tuples of the form\n","# (learning_rate, regularization_strength) to tuples of the form\n","# (training_accuracy, validation_accuracy). The accuracy is simply the fraction\n","# of data points that are correctly classified.\n","results = {}\n","best_val = -1   # The highest validation accuracy that we have seen so far.\n","best_svm = None # The LinearSVM object that achieved the highest validation rate.\n","learning_rates = [1e-3,1e-2,1e-1] # learning rate candidates, e.g. [1e-3, 1e-2, ...]\n","regularization_strengths = [1e-3,1e-2,1e-1] # regularization strengths candidates e.g. [1e0, 1e1, ...]\n","\n","################################################################################\n","# TODO:                                                                        #\n","# Write code that chooses the best hyperparameters by tuning on the validation #\n","# set. For each combination of hyperparameters, train a linear SVM on the      #\n","# training set, compute its accuracy on the training and validation sets, and  #\n","# store these numbers in the results dictionary. In addition, store the best   #\n","# validation accuracy in best_val and the LinearSVM object that achieves this  #\n","# accuracy in best_svm.                                                        #\n","#                                                                              #\n","# Hint: You should use a small value for num_iters as you develop your         #\n","# validation code so that the SVMs don't take much time to train; once you are #\n","# confident that your validation code works, you should rerun the validation   #\n","# code with a larger value for num_iters.                                      #\n","################################################################################\n","# Replace \"pass\" statement with your code\n","grid_search = [(lr, rg) for lr in learning_rates for rg in regularization_strengths]\n","X_train = data_dict['X_train'].double()\n","y_train = data_dict['y_train']\n","X_val = data_dict['X_val'].double()\n","y_val = data_dict['y_val']\n","\n","for lr, rg in grid_search:\n","    svm = LinearSVM()\n","\n","    train_loss = svm.train(X_train, y_train, learning_rate=lr, reg=rg, num_iters=2000,\n","                           batch_size=200, verbose=False)\n","    y_train_preds = svm.predict(X_train)\n","\n","    train_accuracy = torch.mean((y_train == y_train_preds).float())\n","\n","    y_val_preds = svm.predict(X_val)\n","    val_accuracy = torch.mean((y_val == y_val_preds).float())\n","\n","    results[(lr, rg)] = (train_accuracy, val_accuracy)\n","    if best_val < val_accuracy:\n","        best_val = val_accuracy\n","        best_svm = svm\n","################################################################################\n","#                              END OF YOUR CODE                                #\n","################################################################################\n","    \n","# Print out results.\n","for lr, reg in sorted(results):\n","  train_accuracy, val_accuracy = results[(lr, reg)]\n","  print('lr %e reg %e train accuracy: %f val accuracy: %f' % (\n","         lr, reg, train_accuracy, val_accuracy))\n","    \n","print('best validation accuracy achieved during cross-validation: %f' % best_val)"],"execution_count":19,"outputs":[{"output_type":"stream","text":["lr 1.000000e-03 reg 1.000000e-03 train accuracy: 0.338633 val accuracy: 0.352000\n","lr 1.000000e-03 reg 1.000000e-02 train accuracy: 0.338510 val accuracy: 0.346000\n","lr 1.000000e-03 reg 1.000000e-01 train accuracy: 0.335714 val accuracy: 0.356000\n","lr 1.000000e-02 reg 1.000000e-03 train accuracy: 0.328224 val accuracy: 0.336000\n","lr 1.000000e-02 reg 1.000000e-02 train accuracy: 0.339490 val accuracy: 0.337000\n","lr 1.000000e-02 reg 1.000000e-01 train accuracy: 0.334122 val accuracy: 0.363000\n","lr 1.000000e-01 reg 1.000000e-03 train accuracy: 0.313204 val accuracy: 0.318000\n","lr 1.000000e-01 reg 1.000000e-02 train accuracy: 0.309490 val accuracy: 0.310000\n","lr 1.000000e-01 reg 1.000000e-01 train accuracy: 0.255735 val accuracy: 0.267000\n","best validation accuracy achieved during cross-validation: 0.363000\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"wBbvJvMeGZ-7"},"source":["Visualize the cross-validation results. You can use this as a debugging tool -- after examining the cross-validation results here, you may want to go back and rerun your cross-validation from above."]},{"cell_type":"code","metadata":{"id":"QbPffK9H_ZGj","executionInfo":{"status":"ok","timestamp":1601456137184,"user_tz":-480,"elapsed":1912,"user":{"displayName":"young Zhao","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuSwIMBj1x2bJOgTT5-3wA8Yt5zgRoz7viNvKn=s64","userId":"11870005096077095950"}},"outputId":"583c536f-3ee8-420d-a538-82de0c480b3c","colab":{"base_uri":"https://localhost:8080/","height":683}},"source":["x_scatter = [math.log10(x[0]) for x in results]\n","y_scatter = [math.log10(x[1]) for x in results]\n","\n","# plot training accuracy\n","marker_size = 100\n","colors = [results[x][0] for x in results]\n","plt.scatter(x_scatter, y_scatter, marker_size, c=colors, cmap='viridis')\n","plt.colorbar()\n","plt.xlabel('log learning rate')\n","plt.ylabel('log regularization strength')\n","plt.title('CIFAR-10 training accuracy')\n","plt.gcf().set_size_inches(8, 5)\n","plt.show()\n","\n","# plot validation accuracy\n","colors = [results[x][1] for x in results] # default size of markers is 20\n","plt.scatter(x_scatter, y_scatter, marker_size, c=colors, cmap='viridis')\n","plt.colorbar()\n","plt.xlabel('log learning rate')\n","plt.ylabel('log regularization strength')\n","plt.title('CIFAR-10 validation accuracy')\n","plt.gcf().set_size_inches(8, 5)\n","plt.show()"],"execution_count":20,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 576x360 with 2 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\n","text/plain":["<Figure size 576x360 with 2 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"MCMVzxquGf1O"},"source":["Evaluate the best svm on test set. To get full credit for the assignment you should achieve a test-set accuracy above 35%.\n","\n","(Our best was over 38% -- did you beat us?)"]},{"cell_type":"code","metadata":{"id":"maJ7use3_soL","executionInfo":{"status":"ok","timestamp":1601456143262,"user_tz":-480,"elapsed":1226,"user":{"displayName":"young Zhao","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuSwIMBj1x2bJOgTT5-3wA8Yt5zgRoz7viNvKn=s64","userId":"11870005096077095950"}},"outputId":"f76944c5-b130-4cde-a947-2ddb56cbb13f","colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["y_test_pred = best_svm.predict(data_dict['X_test'].double())\n","test_accuracy = torch.mean((data_dict['y_test'] == y_test_pred).float())\n","print('linear SVM on raw pixels final test set accuracy: %f' % test_accuracy)"],"execution_count":21,"outputs":[{"output_type":"stream","text":["linear SVM on raw pixels final test set accuracy: 0.328200\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"I-QVIG4fGiqJ"},"source":["Visualize the learned weights for each class. Depending on your choice of learning rate and regularization strength, these may or may not be nice to look at."]},{"cell_type":"code","metadata":{"id":"McLHYtFd_vSI","executionInfo":{"status":"ok","timestamp":1601456147995,"user_tz":-480,"elapsed":1664,"user":{"displayName":"young Zhao","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuSwIMBj1x2bJOgTT5-3wA8Yt5zgRoz7viNvKn=s64","userId":"11870005096077095950"}},"outputId":"90045f32-463c-492c-ae0e-26e410d84488","colab":{"base_uri":"https://localhost:8080/","height":380}},"source":["w = best_svm.W[:-1,:] # strip out the bias\n","w = w.reshape(3, 32, 32, 10)\n","w = w.transpose(0, 2).transpose(1, 0)\n","\n","w_min, w_max = torch.min(w), torch.max(w)\n","classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n","for i in range(10):\n","  plt.subplot(2, 5, i + 1)\n","\n","  # Rescale the weights to be between 0 and 255\n","  wimg = 255.0 * (w[:, :, :, i].squeeze() - w_min) / (w_max - w_min)\n","  plt.imshow(wimg.type(torch.uint8).cpu())\n","  plt.axis('off')\n","  plt.title(classes[i])"],"execution_count":22,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 720x576 with 10 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"DkuwyMY27RxS"},"source":["## Softmax Classifier\n","\n","Similar to the SVM, you will:\n","\n","- implement a fully-vectorized **loss function** for the Softmax classifier\n","- implement the fully-vectorized expression for its **analytic gradient**\n","- **check your implementation** with numerical gradient\n","- use a validation set to **tune the learning rate and regularization** strength\n","- **optimize** the loss function with **SGD**\n","- **visualize** the final learned weights"]},{"cell_type":"markdown","metadata":{"id":"hLJMVGtvIgo3"},"source":["First, let's start from implementing the naive softmax loss function with nested loops.\n"]},{"cell_type":"code","metadata":{"id":"w3MDCs7Cwss8","executionInfo":{"status":"ok","timestamp":1601456151985,"user_tz":-480,"elapsed":1065,"user":{"displayName":"young Zhao","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuSwIMBj1x2bJOgTT5-3wA8Yt5zgRoz7viNvKn=s64","userId":"11870005096077095950"}}},"source":["def softmax_loss_naive(W, X, y, reg):\n","  \"\"\"\n","  Softmax loss function, naive implementation (with loops).  When you implment \n","  the regularization over W, please DO NOT multiply the regularization term by \n","  1/2 (no coefficient). \n","\n","  Inputs have dimension D, there are C classes, and we operate on minibatches\n","  of N examples.\n","\n","  Inputs:\n","  - W: A PyTorch tensor of shape (D, C) containing weights.\n","  - X: A PyTorch tensor of shape (N, D) containing a minibatch of data.\n","  - y: A PyTorch tensor of shape (N,) containing training labels; y[i] = c means\n","    that X[i] has label c, where 0 <= c < C.\n","  - reg: (float) regularization strength\n","\n","  Returns a tuple of:\n","  - loss as single float\n","  - gradient with respect to weights W; an tensor of same shape as W\n","  \"\"\"\n","  # Initialize the loss and gradient to zero.\n","  loss = 0.0\n","  dW = torch.zeros_like(W) # D x c\n","\n","  #############################################################################\n","  # TODO: Compute the softmax loss and its gradient using explicit loops.     #\n","  # Store the loss in loss and the gradient in dW. If you are not careful     #\n","  # here, it is easy to run into numeric instability (Check Numeric Stability #\n","  # in http://cs231n.github.io/linear-classify/). Plus, don't forget the      #\n","  # regularization!                                                           #\n","  #############################################################################\n","  # Replace \"pass\" statement with your code\n","  num_train, num_classes = X.shape[0], W.shape[1]\n","  scores = X.mm(W)\n","  # prevent numeric instability\n","  max_score = torch.max(scores,dim=1).values.reshape(-1,1)\n","  scores -= max_score\n","\n","  for i in range(num_train):\n","      log_sum = 0.0\n","      for j in range(num_classes):\n","          log_sum += torch.exp(scores[i,j])\n","      loss += -1 * scores[i,y[i]] + torch.log(log_sum)\n","\n","      for j in range(num_classes):\n","          dW[:, j] += (1/log_sum) * torch.exp(scores[i,j]) * X[i]\n","          if j == y[i]:\n","              dW[:, j] -= X[i]\n","  loss /= num_train\n","  dW /= num_train\n","  loss += reg * torch.sum(W * W)\n","  dW += reg * 2 * W\n","  #############################################################################\n","  #                          END OF YOUR CODE                                 #\n","  #############################################################################\n","  return loss, dW"],"execution_count":23,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"cER8fiSq7Ys-"},"source":["As a sanity check to see whether we have implemented the loss correctly, run the softmax classifier with a small random weight matrix and no regularization. You should see loss near log(10) = 2.3"]},{"cell_type":"code","metadata":{"id":"V9q77O7F7VI6","executionInfo":{"status":"ok","timestamp":1601456157686,"user_tz":-480,"elapsed":1193,"user":{"displayName":"young Zhao","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuSwIMBj1x2bJOgTT5-3wA8Yt5zgRoz7viNvKn=s64","userId":"11870005096077095950"}},"outputId":"e9b559e5-1aee-4432-f36d-66d6e6e267b4","colab":{"base_uri":"https://localhost:8080/","height":53}},"source":["# Generate a random softmax weight tensor and use it to compute the loss.\n","coutils.utils.fix_random_seed()\n","W = 0.0001 * torch.randn(3073, 10, device=data_dict['X_val'].device).double()\n","\n","X_batch = data_dict['X_val'][:128].double()\n","y_batch = data_dict['y_val'][:128]\n","\n","# Complete the implementation of softmax_loss_naive and implement a (naive)\n","# version of the gradient that uses nested loops.\n","loss, grad = softmax_loss_naive(W, X_batch, y_batch, reg=0.0)\n","\n","# As a rough sanity check, our loss should be something close to log(10.0).\n","print('loss: %f' % loss)\n","print('sanity check: %f' % (math.log(10.0)))"],"execution_count":24,"outputs":[{"output_type":"stream","text":["loss: 2.302600\n","sanity check: 2.302585\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"5QJzUHl5I0HH"},"source":["Next, we use gradient checking to debug the analytic gradient of our naive softmax loss function. If you've implemented the gradient correctly, you should see relative errors less than `1e-6`.\n"]},{"cell_type":"code","metadata":{"id":"Lj6YpN3q1hVG","executionInfo":{"status":"ok","timestamp":1601456165908,"user_tz":-480,"elapsed":5224,"user":{"displayName":"young Zhao","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuSwIMBj1x2bJOgTT5-3wA8Yt5zgRoz7viNvKn=s64","userId":"11870005096077095950"}},"outputId":"a9adf8e6-845c-4ec2-b0ef-f8c8e48dd8e1","colab":{"base_uri":"https://localhost:8080/","height":197}},"source":["coutils.utils.fix_random_seed()\n","W = 0.0001 * torch.randn(3073, 10, device=data_dict['X_val'].device).double()\n","X_batch = data_dict['X_val'][:128].double()\n","y_batch = data_dict['y_val'][:128]\n","\n","loss, grad = softmax_loss_naive(W, X_batch, y_batch, reg=0.0)\n","\n","f = lambda w: softmax_loss_naive(w, X_batch, y_batch, reg=0.0)[0]\n","grad_check_sparse(f, W, grad, 10)"],"execution_count":25,"outputs":[{"output_type":"stream","text":["numerical: 0.008387 analytic: 0.008387, relative error: 4.265750e-09\n","numerical: 0.009227 analytic: 0.009227, relative error: 3.472402e-07\n","numerical: -0.002471 analytic: -0.002471, relative error: 1.176265e-06\n","numerical: -0.003144 analytic: -0.003144, relative error: 6.375857e-07\n","numerical: 0.006011 analytic: 0.006011, relative error: 4.860090e-07\n","numerical: 0.005936 analytic: 0.005936, relative error: 5.008406e-07\n","numerical: 0.015703 analytic: 0.015703, relative error: 1.198765e-07\n","numerical: 0.006452 analytic: 0.006452, relative error: 1.699099e-07\n","numerical: -0.015533 analytic: -0.015533, relative error: 4.257914e-08\n","numerical: -0.010170 analytic: -0.010170, relative error: 2.474806e-07\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"cFcgeajBI-L3"},"source":["Let's perform another gradient check with regularization enabled. Again you should see relative errors less than `1e-6`."]},{"cell_type":"code","metadata":{"id":"Ik0i21sszZzg","executionInfo":{"status":"ok","timestamp":1601456172841,"user_tz":-480,"elapsed":5668,"user":{"displayName":"young Zhao","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuSwIMBj1x2bJOgTT5-3wA8Yt5zgRoz7viNvKn=s64","userId":"11870005096077095950"}},"outputId":"88cf4855-10b0-431a-a971-816dbee6d080","colab":{"base_uri":"https://localhost:8080/","height":197}},"source":["coutils.utils.fix_random_seed()\n","W = 0.0001 * torch.randn(3073, 10, device=data_dict['X_val'].device).double()\n","reg = 10.0\n","\n","X_batch = data_dict['X_val'][:128].double()\n","y_batch = data_dict['y_val'][:128]\n","\n","loss, grad = softmax_loss_naive(W, X_batch, y_batch, reg)\n","\n","f = lambda w: softmax_loss_naive(w, X_batch, y_batch, reg)[0]\n","grad_check_sparse(f, W, grad, 10)"],"execution_count":26,"outputs":[{"output_type":"stream","text":["numerical: 0.007517 analytic: 0.007517, relative error: 8.089159e-08\n","numerical: 0.008167 analytic: 0.008167, relative error: 4.198667e-07\n","numerical: -0.002551 analytic: -0.002551, relative error: 9.969608e-07\n","numerical: -0.000841 analytic: -0.000841, relative error: 2.372480e-06\n","numerical: 0.002228 analytic: 0.002228, relative error: 1.088308e-06\n","numerical: 0.005460 analytic: 0.005460, relative error: 4.646146e-07\n","numerical: 0.015762 analytic: 0.015762, relative error: 1.405207e-07\n","numerical: 0.007097 analytic: 0.007097, relative error: 1.473214e-07\n","numerical: -0.015532 analytic: -0.015532, relative error: 7.197051e-08\n","numerical: -0.011201 analytic: -0.011201, relative error: 2.241145e-07\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"JQgRzrdRJAm7"},"source":["Then, let's move on to the vectorized form"]},{"cell_type":"code","metadata":{"id":"bYlTPinzwv3x","executionInfo":{"status":"ok","timestamp":1601457267791,"user_tz":-480,"elapsed":893,"user":{"displayName":"young Zhao","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuSwIMBj1x2bJOgTT5-3wA8Yt5zgRoz7viNvKn=s64","userId":"11870005096077095950"}}},"source":["def softmax_loss_vectorized(W, X, y, reg):\n","  \"\"\"\n","  Softmax loss function, vectorized version.  When you implment the \n","  regularization over W, please DO NOT multiply the regularization term by 1/2 \n","  (no coefficient). \n","\n","  Inputs and outputs are the same as softmax_loss_naive.\n","  \"\"\"\n","  # Initialize the loss and gradient to zero.\n","  loss = 0.0\n","  dW = torch.zeros_like(W)\n","\n","  #############################################################################\n","  # TODO: Compute the softmax loss and its gradient using no explicit loops.  #\n","  # Store the loss in loss and the gradient in dW. If you are not careful     #\n","  # here, it is easy to run into numeric instability (Check Numeric Stability #\n","  # in http://cs231n.github.io/linear-classify/). Don't forget the            #\n","  # regularization!                                                           #\n","  #############################################################################\n","  # Replace \"pass\" statement with your code\n","  scores = X.mm(W) # N x c -> #example x #classes\n","  max_score = torch.max(scores,dim=1).values.reshape(-1,1)\n","  scores -= max_score\n","  num_train = X.shape[0]\n","  num_classes = W.shape[1]\n","  \n","  exp_scores = torch.exp(scores)\n","  sum_exp_scores = exp_scores.sum(dim=1)\n","  # print(sum_exp_scores.shape)\n","  tmp = scores[range(num_train), y]\n","  print(tmp.shape)\n","\n","  loss = -1 * scores[range(num_train), y] + torch.log(sum_exp_scores)\n","  print(loss.shape)\n","  loss = torch.sum(loss)\n","  loss /= num_train\n","  loss += reg * torch.sum(W*W)\n","\n","  correct_class = torch.zeros((num_train, num_classes), dtype=X.dtype).cuda()\n","  correct_class[range(num_train), y] = -1\n","  dW_item1 = X.t().mm(correct_class)  # D x #classes\n","  dW_item2 = (X.t() / sum_exp_scores).mm(exp_scores)\n","  dW = dW_item1 + dW_item2\n","  dW /= num_train\n","  dW += 2 * reg * W\n","\n","  #############################################################################\n","  #                          END OF YOUR CODE                                 #\n","  #############################################################################\n","\n","  return loss, dW"],"execution_count":36,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"88xZ0rbLJGKV"},"source":["Now that we have a naive implementation of the softmax loss function and its gradient, implement a vectorized version in softmax_loss_vectorized. The two versions should compute the same results, but the vectorized version should be much faster.\n","\n","The differences between the naive and vectorized losses and gradients should both be less than `1e-6`, and your vectorized implementation should be at least 100x faster than the naive implementation."]},{"cell_type":"code","metadata":{"id":"lGNAe-oP1dds","executionInfo":{"status":"ok","timestamp":1601457270643,"user_tz":-480,"elapsed":1121,"user":{"displayName":"young Zhao","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuSwIMBj1x2bJOgTT5-3wA8Yt5zgRoz7viNvKn=s64","userId":"11870005096077095950"}},"outputId":"c1b92518-fd24-4a6c-9ab9-8e4962fbfee3","colab":{"base_uri":"https://localhost:8080/","height":143}},"source":["coutils.utils.fix_random_seed()\n","W = 0.0001 * torch.randn(3073, 10, device=data_dict['X_val'].device)\n","reg = 0.05\n","\n","X_batch = data_dict['X_val'][:128]\n","y_batch = data_dict['y_val'][:128]\n","\n","# Run and time the naive version\n","torch.cuda.synchronize()\n","tic = time.time()\n","loss_naive, grad_naive = softmax_loss_naive(W, X_batch, y_batch, reg)\n","torch.cuda.synchronize()\n","toc = time.time()\n","ms_naive = 1000.0 * (toc - tic)\n","print('naive loss: %e computed in %fs' % (loss_naive, ms_naive))\n","\n","# Run and time the vectorized version\n","torch.cuda.synchronize()\n","tic = time.time()\n","loss_vec, grad_vec = softmax_loss_vectorized(W, X_batch, y_batch, reg)\n","torch.cuda.synchronize()\n","toc = time.time()\n","ms_vec = 1000.0 * (toc - tic)\n","print('vectorized loss: %e computed in %fs' % (loss_vec, ms_vec))\n","\n","# we use the Frobenius norm to compare the two versions of the gradient.\n","loss_diff = (loss_naive - loss_vec).abs().item()\n","grad_diff = torch.norm(grad_naive - grad_vec, p='fro')\n","print('Loss difference: %.2e' % loss_diff)\n","print('Gradient difference: %.2e' % grad_diff)\n","print('Speedup: %.2fX' % (ms_naive / ms_vec))"],"execution_count":37,"outputs":[{"output_type":"stream","text":["naive loss: 2.302615e+00 computed in 211.249352s\n","torch.Size([128])\n","torch.Size([128])\n","vectorized loss: 2.302616e+00 computed in 1.768112s\n","Loss difference: 4.77e-07\n","Gradient difference: 3.23e-07\n","Speedup: 119.48X\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"bqZScXKyq6WB"},"source":["Let's check that your implementation of the softmax loss is numerically stable.\n","\n","If either of the following print `nan` then you should double-check the numeric stability of your implementations."]},{"cell_type":"code","metadata":{"id":"bCyFPWxxq58R","executionInfo":{"status":"ok","timestamp":1601349380937,"user_tz":-480,"elapsed":1185,"user":{"displayName":"young Zhao","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuSwIMBj1x2bJOgTT5-3wA8Yt5zgRoz7viNvKn=s64","userId":"11870005096077095950"}},"outputId":"0b99172b-d957-4b8e-84e3-d2af6c1d1d47","colab":{"base_uri":"https://localhost:8080/","height":53}},"source":["device = data_dict['X_train'].device\n","dtype = torch.float32\n","D = data_dict['X_train'].shape[1]\n","C = 10\n","\n","W_ones = torch.ones(D, C, device=device, dtype=dtype)\n","W, loss_hist = train_linear_classifier(softmax_loss_naive, W_ones, \n","                                       data_dict['X_train'], \n","                                       data_dict['y_train'], \n","                                       learning_rate=1e-8, reg=2.5e4,\n","                                       num_iters=1, verbose=True)\n","\n","\n","W_ones = torch.ones(D, C, device=device, dtype=dtype)\n","W, loss_hist = train_linear_classifier(softmax_loss_vectorized, W_ones, \n","                                       data_dict['X_train'], \n","                                       data_dict['y_train'], \n","                                       learning_rate=1e-8, reg=2.5e4,\n","                                       num_iters=1, verbose=True)\n"],"execution_count":null,"outputs":[{"output_type":"stream","text":["iteration 0 / 1: loss 768249984.000000\n","iteration 0 / 1: loss 768249984.000000\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"kR4JGKoek8FB"},"source":["Now lets train a softmax classifier with some default hyperparameters:\n"]},{"cell_type":"code","metadata":{"id":"Kqga1rvjk7b8","executionInfo":{"status":"ok","timestamp":1601349396080,"user_tz":-480,"elapsed":1619,"user":{"displayName":"young Zhao","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuSwIMBj1x2bJOgTT5-3wA8Yt5zgRoz7viNvKn=s64","userId":"11870005096077095950"}},"outputId":"4831b9fd-463d-41c8-ba96-5a9cff3f83ca","colab":{"base_uri":"https://localhost:8080/","height":305}},"source":["# fix random seed before we perform this operation\n","coutils.utils.fix_random_seed(10)\n","\n","torch.cuda.synchronize()\n","tic = time.time()\n","\n","W, loss_hist = train_linear_classifier(softmax_loss_vectorized, None, \n","                                       data_dict['X_train'], \n","                                       data_dict['y_train'], \n","                                       learning_rate=1e-10, reg=2.5e4,\n","                                       num_iters=1500, verbose=True)\n","\n","torch.cuda.synchronize()\n","toc = time.time()\n","print('That took %fs' % (toc - tic))"],"execution_count":null,"outputs":[{"output_type":"stream","text":["iteration 0 / 1500: loss 2.303346\n","iteration 100 / 1500: loss 2.303344\n","iteration 200 / 1500: loss 2.303344\n","iteration 300 / 1500: loss 2.303342\n","iteration 400 / 1500: loss 2.303342\n","iteration 500 / 1500: loss 2.303342\n","iteration 600 / 1500: loss 2.303342\n","iteration 700 / 1500: loss 2.303341\n","iteration 800 / 1500: loss 2.303338\n","iteration 900 / 1500: loss 2.303339\n","iteration 1000 / 1500: loss 2.303339\n","iteration 1100 / 1500: loss 2.303336\n","iteration 1200 / 1500: loss 2.303335\n","iteration 1300 / 1500: loss 2.303337\n","iteration 1400 / 1500: loss 2.303334\n","That took 1.074720s\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"QKjxCGwkorCc"},"source":["Plot the loss curve:"]},{"cell_type":"code","metadata":{"id":"K29x-DWNoujL","executionInfo":{"status":"ok","timestamp":1601349405138,"user_tz":-480,"elapsed":1006,"user":{"displayName":"young Zhao","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuSwIMBj1x2bJOgTT5-3wA8Yt5zgRoz7viNvKn=s64","userId":"11870005096077095950"}},"outputId":"66bcc1e4-6a14-44de-fd78-f6a9115d564a","colab":{"base_uri":"https://localhost:8080/","height":508}},"source":["plt.plot(loss_hist, 'o')\n","plt.xlabel('Iteration number')\n","plt.ylabel('Loss value')\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 720x576 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"7WvpBuJWSwfd"},"source":["Let's compute the accuracy of current model. It should be less than 10%."]},{"cell_type":"code","metadata":{"id":"zb8kY2MjSvfH","executionInfo":{"status":"ok","timestamp":1601349409145,"user_tz":-480,"elapsed":820,"user":{"displayName":"young Zhao","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuSwIMBj1x2bJOgTT5-3wA8Yt5zgRoz7viNvKn=s64","userId":"11870005096077095950"}},"outputId":"bb909978-8658-4b62-c62a-f14efac05af2","colab":{"base_uri":"https://localhost:8080/","height":53}},"source":["# evaluate the performance on both the training and validation set\n","y_train_pred = predict_linear_classifier(W, data_dict['X_train'])\n","train_acc = 100.0 * (data_dict['y_train'] == y_train_pred).float().mean().item()\n","print('training accuracy: %.2f%%' % train_acc)\n","y_val_pred = predict_linear_classifier(W, data_dict['X_val'])\n","val_acc = 100.0 * (data_dict['y_val'] == y_val_pred).float().mean().item()\n","print('validation accuracy: %.2f%%' % val_acc)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["training accuracy: 8.43%\n","validation accuracy: 8.00%\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"IuV0BZvzJirI"},"source":["Now use the validation set to tune hyperparameters (regularization strength and learning rate). You should experiment with different ranges for the learning rates and regularization strengths.\n","\n","To get full credit for the assignment, your best model found through cross-validation should achieve an accuracy above 0.37 on the validation set.\n","\n","(Our best model was above 0.40 -- did you beat us?)"]},{"cell_type":"code","metadata":{"id":"TGyf3TkWB-Er"},"source":["class Softmax(LinearClassifier):\n","  \"\"\" A subclass that uses the Softmax + Cross-entropy loss function \"\"\"\n","  def loss(self, W, X_batch, y_batch, reg):\n","    return softmax_loss_vectorized(W, X_batch, y_batch, reg)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"68lmNVj31ddu","executionInfo":{"status":"ok","timestamp":1601350890344,"user_tz":-480,"elapsed":37643,"user":{"displayName":"young Zhao","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuSwIMBj1x2bJOgTT5-3wA8Yt5zgRoz7viNvKn=s64","userId":"11870005096077095950"}},"outputId":"c7017475-032b-4595-b02b-73348d7196b2","colab":{"base_uri":"https://localhost:8080/","height":323}},"source":["results = {}\n","best_val = -1\n","best_softmax = None\n","\n","learning_rates = [1e-3, 1e-2, 1e-1,3*1e-1] # learning rate candidates\n","regularization_strengths = [1e-3, 1e-2, 1e-1, 3*1e-1] # regularization strengths candidates\n","\n","# As before, store your cross-validation results in this dictionary.\n","# The keys should be tuples of (learning_rate, regularization_strength) and\n","# the values should be tuples (train_accuracy, val_accuracy)\n","results = {}\n","\n","################################################################################\n","# TODO:                                                                        #\n","# Use the validation set to set the learning rate and regularization strength. #\n","# This should be similar to the cross-validation that you used for the SVM,    #\n","# but you may need to select different hyperparameters to achieve good         #\n","# performance with the softmax classifier. Save your best trained softmax      #\n","# classifer in best_softmax.                                                   # \n","################################################################################\n","# Replace \"pass\" statement with your code\n","research = [(lr, rs) for lr in learning_rates for rs in regularization_strengths]\n","X_train = data_dict['X_train'].double()\n","y_train = data_dict['y_train']\n","X_val = data_dict['X_val'].double()\n","y_val = data_dict['y_val']\n","\n","for (lr, rs) in research:\n","  classifier = Softmax()\n","  train_loss = classifier.train(X_train, y_train, reg=rs,learning_rate=lr,\n","                                num_iters=1500,batch_size=200,verbose=False)\n","  y_train_pred = classifier.predict(X_train)\n","  train_accuracy = torch.mean((y_train_pred == y_train).float())\n","\n","  y_val_pred = classifier.predict(X_val)\n","  val_accuracy = torch.mean((y_val_pred == y_val).float())\n","  results[(lr,rs)] = (train_accuracy, val_accuracy)\n","  if best_val < val_accuracy:\n","    best_val = val_accuracy\n","    best_softmax = classifier\n","\n","################################################################################\n","#                              END OF YOUR CODE                                #\n","################################################################################\n","    \n","# Print out results.\n","for lr, reg in sorted(results):\n","    train_accuracy, val_accuracy = results[(lr, reg)]\n","    print('lr %e reg %e train accuracy: %f val accuracy: %f' % (\n","                lr, reg, train_accuracy, val_accuracy))\n","    \n","print('best validation accuracy achieved during cross-validation: %f' % best_val)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["lr 1.000000e-03 reg 1.000000e-03 train accuracy: 0.328245 val accuracy: 0.338000\n","lr 1.000000e-03 reg 1.000000e-02 train accuracy: 0.327980 val accuracy: 0.336000\n","lr 1.000000e-03 reg 1.000000e-01 train accuracy: 0.325653 val accuracy: 0.334000\n","lr 1.000000e-03 reg 3.000000e-01 train accuracy: 0.317531 val accuracy: 0.321000\n","lr 1.000000e-02 reg 1.000000e-03 train accuracy: 0.394714 val accuracy: 0.396000\n","lr 1.000000e-02 reg 1.000000e-02 train accuracy: 0.393510 val accuracy: 0.408000\n","lr 1.000000e-02 reg 1.000000e-01 train accuracy: 0.364265 val accuracy: 0.375000\n","lr 1.000000e-02 reg 3.000000e-01 train accuracy: 0.334347 val accuracy: 0.346000\n","lr 1.000000e-01 reg 1.000000e-03 train accuracy: 0.426714 val accuracy: 0.406000\n","lr 1.000000e-01 reg 1.000000e-02 train accuracy: 0.406102 val accuracy: 0.399000\n","lr 1.000000e-01 reg 1.000000e-01 train accuracy: 0.345633 val accuracy: 0.370000\n","lr 1.000000e-01 reg 3.000000e-01 train accuracy: 0.318714 val accuracy: 0.310000\n","lr 3.000000e-01 reg 1.000000e-03 train accuracy: 0.413102 val accuracy: 0.380000\n","lr 3.000000e-01 reg 1.000000e-02 train accuracy: 0.378755 val accuracy: 0.376000\n","lr 3.000000e-01 reg 1.000000e-01 train accuracy: 0.289041 val accuracy: 0.288000\n","lr 3.000000e-01 reg 3.000000e-01 train accuracy: 0.241000 val accuracy: 0.222000\n","best validation accuracy achieved during cross-validation: 0.408000\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"efougAmNCFLo"},"source":["Run the following to visualize your cross-validation results:"]},{"cell_type":"code","metadata":{"id":"IVhRe3-DBjPr","executionInfo":{"status":"ok","timestamp":1601350701529,"user_tz":-480,"elapsed":1329,"user":{"displayName":"young Zhao","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuSwIMBj1x2bJOgTT5-3wA8Yt5zgRoz7viNvKn=s64","userId":"11870005096077095950"}},"outputId":"62844721-f741-4707-f265-5712d43473c7","colab":{"base_uri":"https://localhost:8080/","height":683}},"source":["x_scatter = [math.log10(x[0]) for x in results]\n","y_scatter = [math.log10(x[1]) for x in results]\n","\n","# plot training accuracy\n","marker_size = 100\n","colors = [results[x][0] for x in results]\n","plt.scatter(x_scatter, y_scatter, marker_size, c=colors, cmap='viridis')\n","plt.colorbar()\n","plt.xlabel('log learning rate')\n","plt.ylabel('log regularization strength')\n","plt.title('CIFAR-10 training accuracy')\n","plt.gcf().set_size_inches(8, 5)\n","plt.show()\n","\n","# plot validation accuracy\n","colors = [results[x][1] for x in results] # default size of markers is 20\n","plt.scatter(x_scatter, y_scatter, marker_size, c=colors, cmap='viridis')\n","plt.colorbar()\n","plt.xlabel('log learning rate')\n","plt.ylabel('log regularization strength')\n","plt.title('CIFAR-10 validation accuracy')\n","plt.gcf().set_size_inches(8, 5)\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 576x360 with 2 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\n","text/plain":["<Figure size 576x360 with 2 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"fbOlUcv6J7MM"},"source":["Them, evaluate the performance of your best model on test set. To get full credit for this assignment you should achieve a test-set accuracy above 0.36.\n","\n","(Our best was just over 0.40 -- did you beat us?)\n","\n"]},{"cell_type":"code","metadata":{"id":"-wxkVdB-1ddx","executionInfo":{"status":"ok","timestamp":1601350801861,"user_tz":-480,"elapsed":779,"user":{"displayName":"young Zhao","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuSwIMBj1x2bJOgTT5-3wA8Yt5zgRoz7viNvKn=s64","userId":"11870005096077095950"}},"outputId":"a977e15d-602a-4c66-bba8-45e48846d16b","colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["y_test_pred = best_softmax.predict(data_dict['X_test'].double())\n","test_accuracy = torch.mean((data_dict['y_test'] == y_test_pred).float())\n","print('softmax on raw pixels final test set accuracy: %f' % (test_accuracy, ))"],"execution_count":null,"outputs":[{"output_type":"stream","text":["softmax on raw pixels final test set accuracy: 0.401700\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"Joo4RbeoKECC"},"source":["Finally, let's visualize the learned weights for each class"]},{"cell_type":"code","metadata":{"id":"XDfxI7mR1ddz","executionInfo":{"status":"ok","timestamp":1601350808643,"user_tz":-480,"elapsed":1203,"user":{"displayName":"young Zhao","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjuSwIMBj1x2bJOgTT5-3wA8Yt5zgRoz7viNvKn=s64","userId":"11870005096077095950"}},"outputId":"74cf51fe-004f-43f9-d05a-4c56942ddcfa","colab":{"base_uri":"https://localhost:8080/","height":380}},"source":["w = best_softmax.W[:-1,:] # strip out the bias\n","w = w.reshape(3, 32, 32, 10)\n","w = w.transpose(0, 2).transpose(1, 0)\n","\n","w_min, w_max = torch.min(w), torch.max(w)\n","\n","classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n","for i in range(10):\n","  plt.subplot(2, 5, i + 1)\n","\n","  # Rescale the weights to be between 0 and 255\n","  wimg = 255.0 * (w[:, :, :, i].squeeze() - w_min) / (w_max - w_min)\n","  plt.imshow(wimg.type(torch.uint8).cpu())\n","  plt.axis('off')\n","  plt.title(classes[i])"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 720x576 with 10 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]}]}